\section{ABiNeS}\label{abines}

In this section the theoretical part of the Adaptive Bilateral Negotiating Strategy for bilateral negotiation (ABiNeS)  is explained.
This negotiation strategy has been chosen for the implementation of this group's agent.\cite{ABiNeS2012}


\subsection{Acceptance threshold}

ABiNeS has different phases during a negotiation session with another agent.
One of these phases decides on whether or not to accept an opponent's bid.
For the use of that, the agent has an acceptance threshold, which is a factor $l$, below which no opponent bids will be accepted.
By maintaining an acceptance threshold, the agent makes sure to only accept bids, which are of good value to it and also tries maintaining a utility above a partners utility, to win a negotiation.
The acceptance threshold is connected to one important variable of the negotiation process: the non-exploitation point $\lambda$, which denotes a certain point in time at which the acceptance threshold will no longer decrease.
As the negotiation proceeds, time $t$ is approaching $\lambda$.
Before time $t$ reaches $\lambda$, the ABiNeS agent tries to exploit the partner as much as possible.
This is achieved by analysing the opponent's concessive degree.
If the opponent makes a lot of different offers, he is considered more concessive.
The more concessive the opponent is considered, the more quickly $\lambda$ increases, so the non-exploitation point is postponed.
$\lambda$ and the acceptance threshold are majorly influenced by various controlling constants as well as the discounting factor $\delta$. The value of the $\delta$ is defined in the preference profile of the domain, thus is not under control of the implementation of the agent. The values of the other constants are set according to experiments and testing procedures. The choice of these values are describes more in detail in chapter \ref{finetuning}.\cite{ABiNeS2012}

\subsection{Bid proposal}
\label{bidproposal}
One of the most important tasks of an agent is to propose a bid.
Herefore the ABiNeS agents tries to construct an opponent-model containing its estimated utility for each bid. 
The estimate gets more precise for every bid the opponent chooses, which is accomplished by applying a reinforcement learning algorithm.
This algorithm is based on the assumption that the opponent proposes a bid more frequently if he has a relatively high preference towards the values of this bid.
Assuming this, bids that are proposed very frequently are considered to have a high utility for the opponent.
To resemble from using the bid utilities, which the opponent concerns as best, the $\epsilon$-$greedy$ algorithm comes to use, to still have an exploration factor on other possible outcomes. This enables the agent to test possibilities, which are not that great for the opponent. Taking everything of the previous mentioned details into account, the agent proposes a bid to the opponent. The factor $\epsilon$ is responsible as to how far the ABiNeS agent tries to explore different bid possibilities.\cite{ABiNeS2012}

\subsection{Offer acceptance}
\label{Offeracceptance}
Another important part of a negotiation is the acceptance of an offer proposed by a negotiating partner.
For this component the ABiNeS agent has an acceptance condition component, which is taking the current acceptance threshold into account to determine if the last offer is acceptable.
If the utility of the last offer of the opponent is above the threshold or above the utility of the bid that would have been offered by the agent itself, the offer is accepted.
If this is not the case, the bid of the entire bid history with the highest utility is taken into account.
If this bid satisfies the previously mentioned condition, the next-bid component is requested to propose this bid the next time, but this last offer is not accepted.\cite{ABiNeS2012}

\subsection{Reservation value}
The reservation value $r$ is defined in every preference profile of a domain and thus, as the discounting factor, not under control of the implementation of the agent.
$r$ denoted the minimum desired utility bid for an agent.
If the threshold ever undercuts $r$, the negotiation is terminated, for there is no reason for the agent to accept or offer a bid with a utility lower than $r$.
If the reservation is low throughout the negotiation it will only be used in the end when time runs out.\cite{ABiNeS2012}

\subsection{Verdict}
An ABiNeS agent follows a well balanced strategy. The prediction of its partners moves as well as the constant savings of the bid history, enables it to perform well in negotiation. It is however important for the opponent to propose bids, which are of high utility to it. This is one of the weak points for ABiNeS, as it relies on the fact that the partner proposes valuable bids more often. If that was not the case, the prediction of the opponent model might be in peril and thus the whole negotiation plan can fail.\cite{ABiNeS2012}